
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "3"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 87 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 83 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	0.54%	1.168099	-0.714124	0.243030	0.684437	0.724158	2.632539	-0.165848	0.792601	0.7242	0.6998	0.2641	3.373258	2.937418
2459593	digital_ok	0.00%	0.00%	0.54%	0.00%	2.69%	0.54%	1.732211	-0.290832	1.046536	1.114425	0.153146	0.210477	0.006226	0.470556	0.6353	0.6115	0.2236	2.606995	2.145906
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.654343	-0.391423	0.096828	-0.518433	-1.505230	-0.298777	-1.908388	0.089930	0.0337	0.0291	0.0018	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	1.709307	0.373187	1.413281	1.733478	0.206681	-0.477004	0.632387	0.012626	0.5243	0.5272	0.3150	1.629596	1.508420
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	1.08%	3.756823	1.214054	1.255596	1.311372	0.170237	0.108604	-0.227036	0.679049	0.5291	0.5293	0.3093	2.109858	1.946774
2459589	digital_ok	0.00%	0.54%	0.54%	0.00%	3.23%	0.00%	-0.223447	-0.537228	0.621228	0.711661	0.780659	-0.073394	1.119149	0.860850	0.6185	0.5929	0.2418	1.933830	1.829176
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	0.938189	-0.650366	1.831512	1.615010	2.097421	1.709058	1.310621	1.576400	0.7025	0.6602	0.2203	3.361568	2.542027
2459587	digital_ok	0.00%	-	-	-	6.99%	0.00%	-0.696702	-0.319900	2.567553	3.164739	-0.799783	-0.773660	0.390459	0.522134	nan	nan	nan	1.202253	1.193365
2459586	digital_ok	0.00%	3.24%	7.57%	0.00%	10.27%	0.54%	-1.307103	-0.224961	-0.796043	1.384014	-0.197954	1.052573	-0.382648	-0.849618	0.5259	0.5261	0.2998	1.923559	1.718653
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.848315	1.404518	-0.573642	0.553822	-0.792956	1.519690	-1.130622	1.160145	0.0342	0.0334	0.0016	0.000000	0.000000
2459584	digital_ok	0.00%	36.61%	36.61%	0.00%	38.92%	0.60%	-0.374518	-0.036194	-0.047472	0.466397	0.319042	0.035166	-0.846999	-0.848271	0.4729	0.4788	0.3116	1.624435	1.455060
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	digital_ok	0.00%	8.11%	8.11%	0.00%	12.43%	0.00%	-1.136390	-0.870959	-0.276443	-0.287419	-0.721698	-1.355641	-0.820520	-0.418501	0.5133	0.5167	0.3327	1.774382	1.586291
2459581	digital_ok	0.00%	2.70%	3.78%	0.00%	7.57%	0.00%	-0.950522	-0.995127	-1.028131	-0.071232	-0.982122	-1.081315	-0.516782	0.282219	0.5242	0.5266	0.3332	1.798541	1.562544
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-1.021309	-0.836757	-0.271697	0.096608	-0.491654	-1.018233	-1.011495	-0.003055	0.5302	0.5358	0.3302	1.840215	1.630768
2459579	digital_ok	0.00%	1.62%	1.62%	0.00%	6.45%	0.00%	-1.027319	-1.073389	-0.173376	0.001235	-0.798586	-1.160863	-0.673047	0.135131	0.5262	0.5332	0.3351	2.087592	1.801396
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.268354	0.428118	-0.775734	-0.322823	-0.911921	-0.568277	-0.078845	-0.753696	0.0376	0.0321	0.0034	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-0.812132	-0.845884	-0.809806	-0.267049	-0.779025	-1.034671	-0.554640	-0.551415	0.5316	0.5382	0.3291	1.849169	1.559565
2459576	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.572411	-0.601744	-0.682640	-0.531359	-0.601399	-0.877825	-0.218935	-0.187124	0.0757	0.0607	0.0114	0.831279	0.831773
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459574	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.636862	-0.724433	-0.868860	-0.686407	-0.308392	-0.820734	0.494349	0.854936	0.0958	0.0894	0.0171	1.210580	1.212558
2459573	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.113811	-0.769974	-0.278480	0.167398	-0.495567	-1.374933	-0.693753	0.236216	0.0621	0.0741	0.0085	0.000000	0.000000
2459572	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.037094	0.418676	-0.696535	-0.376686	-1.393502	-0.769764	0.018905	-0.444165	0.0400	0.0318	0.0038	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.703460	-0.725574	-0.803084	-0.445874	-1.149950	-0.673073	-0.660982	-0.430348	0.0744	0.0855	0.0093	1.175144	1.171714
2459566	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.927348	-1.061328	-0.650439	-0.092929	-0.807652	-1.013554	-0.682249	-0.554732	0.0967	0.0827	0.0163	0.000000	0.000000
2459565	digital_ok	0.00%	-	-	-	93.33%	0.00%	-1.019629	-1.019629	-0.885933	-0.885933	-0.752740	-0.752740	-0.803206	-0.803206	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	0.00%	-0.808273	-0.307903	-1.205312	0.166335	-0.559900	1.583986	1.005225	3.172589	0.6921	0.6915	0.4228	1.823398	1.503009
2459563	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.350437	-0.575027	-0.556944	-0.889501	-0.087103	-0.052439	-0.217145	-0.046786	0.6896	0.6936	0.4047	1.857082	1.510883
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	12.37%	1.61%	-0.426511	-0.638708	-0.034572	-0.136898	-1.127013	-0.909004	-0.166912	1.726253	0.6912	0.6924	0.4148	1.780092	1.484880
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0414	0.0321	0.0031	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.482022	-0.824192	-0.037324	-0.134084	-0.656486	-0.808705	0.115082	2.021579	0.6930	0.6934	0.4115	1.794154	1.506062
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6933	0.6923	0.4204	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	68.65%	31.35%	-0.334136	-0.586285	0.090738	0.813650	-0.184875	-0.289005	-0.683188	0.073026	0.7003	0.6934	0.4179	2.598425	0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.161275	-0.484743	-0.647510	-0.968428	-0.448292	-0.006558	0.297846	0.258492	0.6964	0.6932	0.4125	1.961309	1.540039
2459550	digital_ok	-	99.18%	99.18%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0609	0.0554	0.0048	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.081579	-0.663015	-0.538573	0.069866	-0.245394	-0.478240	-0.524992	0.638117	0.6953	0.6924	0.4236	1.897708	1.540398
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	-0.501673	-0.467235	-0.395153	-0.972694	-0.777960	0.941773	0.149366	0.086519	0.7550	0.7415	0.3765	2.017133	1.535347
2459541	digital_ok	0.00%	1.79%	1.79%	0.00%	21.88%	0.00%	-0.307647	-0.511694	0.218438	0.185519	-0.031665	-1.015143	0.288155	0.246638	0.7443	0.7295	0.3487	1.721349	1.425039
2459540	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	-0.183879	-0.902717	-0.128976	0.944401	-0.133175	-0.824808	1.133153	0.857097	0.7186	0.7193	0.4443	1.958327	1.623460
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.318594	-0.537534	-0.523203	-0.845794	-0.594650	-0.655071	-0.391751	0.141462	0.7834	0.7508	0.3930	1.960492	1.567067
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.45%	-0.362926	-0.802417	-0.509342	-0.904451	-0.160109	-0.455459	0.399130	1.001862	0.7736	0.7501	0.3825	2.095889	1.654049
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	0.025316	-0.766557	-0.165055	0.436302	-0.204046	-0.758217	-0.442097	1.372423	0.7135	0.7049	0.4278	1.772929	1.519772
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.56%	-0.204996	-0.824274	-0.040550	0.453244	-0.327311	-1.029021	0.179094	0.450789	0.7127	0.7008	0.4282	1.767692	1.517264
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.488240	-0.793061	-0.502830	-0.790346	-0.103912	0.012291	0.341276	0.224828	0.7283	0.7167	0.4286	1.867989	1.548844
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.172538	-0.587261	-0.255086	0.407158	-0.888239	-0.468742	-0.720815	-0.166176	0.8909	0.9184	0.3570	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	22.45%	0.00%	0.720609	-1.153939	-0.911069	0.604416	-0.908940	0.820144	-0.926531	-0.724199	nan	nan	nan	2.243149	1.860012
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.949654	-0.660812	0.051859	0.732047	-0.036977	1.836970	-0.987076	-0.359228	0.7068	0.6320	0.3864	1.693666	1.313305
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.068230	0.491332	-0.779598	-0.626404	-0.594761	0.622726	-0.147878	-0.321556	0.0382	0.0365	0.0037	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	-0.730845	-0.953615	-0.414500	-0.234631	-1.230572	-0.959920	0.337896	0.275657	0.8949	0.9230	0.2119	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.629894	-1.011203	0.096441	-0.439537	1.538366	-0.596589	0.313564	0.351788	0.8909	0.8512	0.2128	7.402924	5.341381
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	-0.490024	-0.165560	0.248953	0.779794	-1.198486	-0.241475	-0.694522	-0.751400	0.8326	0.8256	0.5074	0.000000	0.000000
2459501	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.648138	-0.155923	0.633271	0.853276	-0.663790	-0.551127	-0.939809	-0.869780	0.1559	0.1287	0.0266	0.000000	0.000000
2459500	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.672825	-0.094256	0.152962	0.532416	-0.464591	-0.312738	-1.046144	-0.673096	0.1714	0.1399	0.0277	0.000000	0.000000
2459499	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.418455	0.063203	0.147928	0.583642	-0.487115	-0.261580	-0.103579	-0.188816	0.1503	0.1368	0.0161	0.000000	0.000000
2459498	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.289474	0.188998	0.346397	0.707791	-0.180768	-0.175042	-0.495940	-0.514293	0.1755	0.1503	0.0357	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.284562	0.296986	0.675366	1.016190	-0.276341	-0.223711	-0.733315	-0.606967	0.1759	0.1670	0.0405	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.677793	0.166508	0.258367	0.737873	-0.656843	-0.536424	-0.794062	-0.662999	0.2228	0.1765	0.0417	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.513230	0.208220	0.089341	0.696599	-0.415830	-0.231109	-1.006624	-0.765571	0.2255	0.1714	0.0471	0.000000	0.000000
2459494	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.403554	0.113696	0.184632	0.688480	-0.617494	-0.608511	-0.140146	-0.239258	0.2066	0.1668	0.0457	0.000000	0.000000
2459492	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	0.146074	-0.658185	0.253149	0.328961	-1.317479	-1.374098	-1.151861	-1.020475	0.7293	0.7910	0.3682	0.192910	0.201469
2459491	digital_ok	0.00%	0.00%	0.00%	0.00%	50.00%	0.00%	-0.090188	-0.580297	0.357388	0.166662	-0.790018	-0.881173	-0.021996	-0.469939	0.7810	0.8120	0.2906	0.629426	0.531179
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	-0.133178	-1.026772	0.140833	0.376081	-0.669060	-1.134214	-0.904391	-0.637846	0.7269	0.7880	0.4056	2.268426	1.803171
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	47.37%	0.00%	0.102407	-0.658519	0.230837	0.126485	-1.182867	-0.508949	-0.252544	-0.290490	0.7536	0.8024	0.3369	2.238368	1.698661
2459488	digital_ok	0.00%	0.00%	0.00%	0.00%	85.71%	14.29%	0.284832	-0.598195	0.219935	0.502571	-1.298796	-1.199436	-0.951864	-0.911806	0.7534	0.8046	0.3843	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 3 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 3, Node 1:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 3: 2459611

Antenna 3: 2459610

Antenna 3: 2459609

Antenna 3: 2459608

Antenna 3: 2459607

Antenna 3: 2459605

Antenna 3: 2459604

Antenna 3: 2459603

Antenna 3: 2459602

Antenna 3: 2459598

Antenna 3: 2459597

Antenna 3: 2459596

Antenna 3: 2459595

Antenna 3: 2459594

Antenna 3: 2459593

Antenna 3: 2459592

Antenna 3: 2459591

Antenna 3: 2459590

Antenna 3: 2459589

Antenna 3: 2459588

Antenna 3: 2459587

Antenna 3: 2459586

Antenna 3: 2459585

Antenna 3: 2459584

Antenna 3: 2459583

Antenna 3: 2459582

Antenna 3: 2459581

Antenna 3: 2459580

Antenna 3: 2459579

Antenna 3: 2459578

Antenna 3: 2459577

Antenna 3: 2459576

Antenna 3: 2459575

Antenna 3: 2459574

Antenna 3: 2459573

Antenna 3: 2459572

Antenna 3: 2459571

Antenna 3: 2459570

Antenna 3: 2459569

Antenna 3: 2459566

Antenna 3: 2459565

Antenna 3: 2459564

Antenna 3: 2459563

Antenna 3: 2459562

Antenna 3: 2459561

Antenna 3: 2459560

Antenna 3: 2459559

Antenna 3: 2459558

Antenna 3: 2459556

Antenna 3: 2459552

Antenna 3: 2459551

Antenna 3: 2459549

Antenna 3: 2459542

Antenna 3: 2459541

Antenna 3: 2459540

Antenna 3: 2459536

Antenna 3: 2459535

Antenna 3: 2459534

Antenna 3: 2459533

Antenna 3: 2459532

Antenna 3: 2459530

Antenna 3: 2459527

Antenna 3: 2459524

Antenna 3: 2459523

Antenna 3: 2459522

Antenna 3: 2459513

Antenna 3: 2459508

Antenna 3: 2459506

Antenna 3: 2459505

Antenna 3: 2459503

Antenna 3: 2459501

Antenna 3: 2459500

Antenna 3: 2459499

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶